{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Tutorial on how to create a convolutional autoencoder w/ Tensorflow.\n",
    "\n",
    "Parag K. Mital, Jan 2016\n",
    "\"\"\"\n",
    "# %% imports\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "from libs.activations import lrelu\n",
    "from libs.utils import corrupt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %%\n",
    "def autoencoder(input_shape=[None, 784],\n",
    "                n_filters=[1, 10, 10, 10],\n",
    "                filter_sizes=[3, 3, 3, 3],\n",
    "                corruption=False):\n",
    "    \"\"\"Build a deep denoising autoencoder w/ tied weights.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    input_shape : list, optional\n",
    "        Description\n",
    "    n_filters : list, optional\n",
    "        Description\n",
    "    filter_sizes : list, optional\n",
    "        Description\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    x : Tensor\n",
    "        Input placeholder to the network\n",
    "    z : Tensor\n",
    "        Inner-most latent representation\n",
    "    y : Tensor\n",
    "        Output reconstruction of the input\n",
    "    cost : Tensor\n",
    "        Overall cost to use for training\n",
    "\n",
    "    Raises\n",
    "    ------\n",
    "    ValueError\n",
    "        Description\n",
    "    \"\"\"\n",
    "    # %%\n",
    "    # input to the network\n",
    "    x = tf.placeholder(\n",
    "        tf.float32, input_shape, name='x')\n",
    "\n",
    "    # %%\n",
    "    # Optionally apply denoising autoencoder\n",
    "    if corruption:\n",
    "        x_noise = corrupt(x)\n",
    "    else:\n",
    "        x_noise = x\n",
    "\n",
    "    # %%\n",
    "    # ensure 2-d is converted to square tensor.\n",
    "    if len(x.get_shape()) == 2:\n",
    "        x_dim = np.sqrt(x_noise.get_shape().as_list()[1])\n",
    "        if x_dim != int(x_dim):\n",
    "            raise ValueError('Unsupported input dimensions')\n",
    "        x_dim = int(x_dim)\n",
    "        x_tensor = tf.reshape(\n",
    "            x_noise, [-1, x_dim, x_dim, n_filters[0]])\n",
    "    elif len(x_noise.get_shape()) == 4:\n",
    "        x_tensor = x_noise\n",
    "    else:\n",
    "        raise ValueError('Unsupported input dimensions')\n",
    "    current_input = x_tensor\n",
    "\n",
    "    # %%\n",
    "    # Build the encoder\n",
    "    encoder = []\n",
    "    shapes = []\n",
    "    for layer_i, n_output in enumerate(n_filters[1:]):\n",
    "        n_input = current_input.get_shape().as_list()[3]\n",
    "        shapes.append(current_input.get_shape().as_list())\n",
    "        W = tf.Variable(\n",
    "            tf.random_uniform([\n",
    "                filter_sizes[layer_i],\n",
    "                filter_sizes[layer_i],\n",
    "                n_input, n_output],\n",
    "                -1.0 / math.sqrt(n_input),\n",
    "                1.0 / math.sqrt(n_input)))\n",
    "        b = tf.Variable(tf.zeros([n_output]))\n",
    "        encoder.append(W)\n",
    "        output = lrelu(\n",
    "            tf.add(tf.nn.conv2d(\n",
    "                current_input, W, strides=[1, 2, 2, 1], padding='SAME'), b))\n",
    "        current_input = output\n",
    "\n",
    "    # %%\n",
    "    # store the latent representation\n",
    "    z = current_input\n",
    "    encoder.reverse()\n",
    "    shapes.reverse()\n",
    "\n",
    "    # %%\n",
    "    # Build the decoder using the same weights\n",
    "    for layer_i, shape in enumerate(shapes):\n",
    "        W = encoder[layer_i]\n",
    "        b = tf.Variable(tf.zeros([W.get_shape().as_list()[2]]))\n",
    "        output = lrelu(tf.add(\n",
    "            tf.nn.conv2d_transpose(\n",
    "                current_input, W,\n",
    "                tf.pack([tf.shape(x)[0], shape[1], shape[2], shape[3]]),\n",
    "                strides=[1, 2, 2, 1], padding='SAME'), b))\n",
    "        current_input = output\n",
    "\n",
    "    # %%\n",
    "    # now have the reconstruction through the network\n",
    "    y = current_input\n",
    "    # cost function measures pixel-wise difference\n",
    "    cost = tf.reduce_sum(tf.square(y - x_tensor))\n",
    "\n",
    "    # %%\n",
    "    return {'x': x, 'z': z, 'y': y, 'cost': cost}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %%\n",
    "def test_mnist():\n",
    "    \"\"\"Test the convolutional autoencder using MNIST.\"\"\"\n",
    "    # %%\n",
    "    import tensorflow as tf\n",
    "    import tensorflow.examples.tutorials.mnist.input_data as input_data\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # %%\n",
    "    # load MNIST as before\n",
    "    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "    mean_img = np.mean(mnist.train.images, axis=0)\n",
    "    ae = autoencoder()\n",
    "\n",
    "    # %%\n",
    "    learning_rate = 0.01\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(ae['cost'])\n",
    "\n",
    "    # %%\n",
    "    # We create a session to use the graph\n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "\n",
    "    # %%\n",
    "    # Fit all training data\n",
    "    batch_size = 100\n",
    "    n_epochs = 10\n",
    "    for epoch_i in range(n_epochs):\n",
    "        for batch_i in range(mnist.train.num_examples // batch_size):\n",
    "            batch_xs, _ = mnist.train.next_batch(batch_size)\n",
    "            train = np.array([img - mean_img for img in batch_xs])\n",
    "            sess.run(optimizer, feed_dict={ae['x']: train})\n",
    "        print(epoch_i, sess.run(ae['cost'], feed_dict={ae['x']: train}))\n",
    "\n",
    "    # %%\n",
    "    # Plot example reconstructions\n",
    "    n_examples = 10\n",
    "    test_xs, _ = mnist.test.next_batch(n_examples)\n",
    "    test_xs_norm = np.array([img - mean_img for img in test_xs])\n",
    "    recon = sess.run(ae['y'], feed_dict={ae['x']: test_xs_norm})\n",
    "    print(recon.shape)\n",
    "    fig, axs = plt.subplots(2, n_examples, figsize=(10, 2))\n",
    "    for example_i in range(n_examples):\n",
    "        axs[0][example_i].imshow(\n",
    "            np.reshape(test_xs[example_i, :], (28, 28)))\n",
    "        axs[1][example_i].imshow(\n",
    "            np.reshape(\n",
    "                np.reshape(recon[example_i, ...], (784,)) + mean_img,\n",
    "                (28, 28)))\n",
    "    fig.show()\n",
    "    plt.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "(0, 1033.2483)\n",
      "(1, 780.23218)\n",
      "(2, 721.82605)\n",
      "(3, 761.09747)\n",
      "(4, 667.92712)\n",
      "(5, 709.97705)\n",
      "(6, 652.50287)\n",
      "(7, 691.22681)\n",
      "(8, 686.44324)\n",
      "(9, 662.02979)\n",
      "(10, 28, 28, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/heythisischo/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py:397: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
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N40TqRtrMeSjhJ2b9Up4/jWoRitIR893oA33g6MMpQ4+tjNHGbZy6pKaxZSZT/igeJUMu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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f042410f450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %%\n",
    "if __name__ == '__main__':\n",
    "    test_mnist()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
